{"id":"W3024801014","doi":"10.1109/access.2020.2994762","title":"CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":770,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Convolutional neural network; Deep learning; Computer science; Artificial intelligence; Classifier (UML); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Pattern recognition (psychology); Radiography; Machine learning; Outbreak; Medicine; Radiology; Virology; Infectious disease (medical specialty); Disease; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003479347,0.0002189913,0.0003343615,0.0001389274,0.0002479909,0.0001749937,0.0005216,0.000153526,0.00008408848],"category_scores_gemma":[0.001670537,0.0002239935,0.00008755999,0.0004776267,0.00007219801,0.0008264801,0.0001555177,0.0002056937,0.00001470302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005196615,"about_ca_system_score_gemma":0.0007977444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007985839,"about_ca_topic_score_gemma":0.0002119141,"domain_scores_codex":[0.9981791,0.00006592155,0.0004005752,0.000779036,0.0002580233,0.0003173632],"domain_scores_gemma":[0.9979922,0.0004505415,0.0002174218,0.000735259,0.0001378007,0.0004667427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002635358,0.0003644962,0.005657532,0.002432925,0.0002990568,0.00005783694,0.000995282,0.003645463,0.8379881,0.000009993832,0.1146032,0.03131077],"study_design_scores_gemma":[0.006680924,0.0004118777,0.001791896,0.0001023616,0.0007375608,0.00002977022,0.0001668781,0.6567687,0.1330918,0.0001319906,0.1996171,0.0004691653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2314605,0.0001342349,0.6436582,0.1188483,0.001790628,0.003089283,0.0003953897,0.000584298,0.00003918534],"genre_scores_gemma":[0.8494298,0.00002753605,0.001648017,0.1472117,0.001120756,0.00008338396,0.0003733813,0.0000683852,0.00003701508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7048963,"threshold_uncertainty_score":0.9134191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3634355217624528,"score_gpt":0.4781321216466249,"score_spread":0.1146965998841722,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}